Setup

We will first begin by loading in the packages we intend to use.

Then, importing the data using a URL directly from the source. This ensures we will capture updates to the data as they come in whenever we run this again.

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.width = 12, fig.height = 8) 

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.1     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggmap)
## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.
library(ggplot2)

import_URL <- read.csv("https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD")

Exploratory Data Analysis (EDA)

Let’s take a look at the dimension of this imported data.frame as well as the variable types of each column.

dim(import_URL)
## [1] 25596    19
str(import_URL)
## 'data.frame':    25596 obs. of  19 variables:
##  $ INCIDENT_KEY           : int  236168668 231008085 230717903 237712309 224465521 228252164 226950018 237710987 224701998 225295736 ...
##  $ OCCUR_DATE             : chr  "11/11/2021" "07/16/2021" "07/11/2021" "12/11/2021" ...
##  $ OCCUR_TIME             : chr  "15:04:00" "22:05:00" "01:09:00" "13:42:00" ...
##  $ BORO                   : chr  "BROOKLYN" "BROOKLYN" "BROOKLYN" "BROOKLYN" ...
##  $ PRECINCT               : int  79 72 79 81 113 113 42 52 34 75 ...
##  $ JURISDICTION_CODE      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ LOCATION_DESC          : chr  "" "" "" "" ...
##  $ STATISTICAL_MURDER_FLAG: chr  "false" "false" "false" "false" ...
##  $ PERP_AGE_GROUP         : chr  "" "45-64" "<18" "" ...
##  $ PERP_SEX               : chr  "" "M" "M" "" ...
##  $ PERP_RACE              : chr  "" "ASIAN / PACIFIC ISLANDER" "BLACK" "" ...
##  $ VIC_AGE_GROUP          : chr  "18-24" "25-44" "25-44" "25-44" ...
##  $ VIC_SEX                : chr  "M" "M" "M" "M" ...
##  $ VIC_RACE               : chr  "BLACK" "ASIAN / PACIFIC ISLANDER" "BLACK" "BLACK" ...
##  $ X_COORD_CD             : num  996313 981845 996546 1001139 1050710 ...
##  $ Y_COORD_CD             : num  187499 171118 187436 192775 184826 ...
##  $ Latitude               : num  40.7 40.6 40.7 40.7 40.7 ...
##  $ Longitude              : num  -74 -74 -74 -73.9 -73.8 ...
##  $ Lon_Lat                : chr  "POINT (-73.95650899099996 40.68131820000008)" "POINT (-74.00866668999998 40.63636384100005)" "POINT (-73.95566903799994 40.68114495900005)" "POINT (-73.939095905 40.69579171600003)" ...

Use GGMAP to Visualize Locations

Here we’re going to visualize the location of each shooting incident using the coordinates given in the dataset, and superimposing them over a background image of the area. We can use the minimum and maximum values of each coordinate to find our map’s bounding box then use ggmap() to do the heavy lifting.

# Initialize the bounding box that will contain the map coordinates.
map_bounds <- c(left = min(import_URL$Longitude), 
        bottom = min(import_URL$Latitude),
        right = max(import_URL$Longitude), 
        top = max(import_URL$Latitude))

# Plot of incident coordinates
incident_map_point <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) + 
        geom_point(data = import_URL, 
                aes(x = Longitude, y = Latitude),
                color = 'darkred', 
                size = 0.25, 
                alpha = 0.2) +
        ggtitle('Point Plot of NYPD Shooting Incident Reporting 2006 - 2021\n    Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>') +
        labs(x = 'Longitude', y = 'Latitude')
## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
incident_map_point

# Density Map to better visualize density of points.
incident_map_density <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) + 
        stat_density2d_filled(data = import_URL, contour_var = 'density',
                aes(x = Longitude, y = Latitude, fill = after_stat(level)), 
                bins = 20, 
                geom = 'polygon', 
                alpha = 0.8) +
        geom_density_2d(data = import_URL, 
                aes(x = Longitude, y = Latitude), 
                bins = 20, 
                alpha = 0.2, 
                color = "white") +
        guides(fill = guide_legend(title = "Density")) + 
        ggtitle('Density Plot of NYPD Shooting Incident Reporting 2006 - 2021\n    Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>') +
        labs(x = 'Longitude', y = 'Latitude')
## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
incident_map_density